import argparse

import torch

from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging


logging.set_verbosity_info()







def       A__   (						snake_case_			:		Tuple							,					snake_case_			:		str							,					snake_case_			:		Tuple   ):
 # Initialise PyTorch model
 SCREAMING_SNAKE_CASE__: List[str]=   LxmertConfig.from_json_file(snake_case_   )
 print(F'Building PyTorch model from configuration: {config}'   )
 SCREAMING_SNAKE_CASE__: List[str]=   LxmertForPreTraining(snake_case_   )

 # Load weights from tf checkpoint
 load_tf_weights_in_lxmert(snake_case_							,					snake_case_							,					snake_case_   )

 # Save pytorch-model
 print(F'Save PyTorch model to {pytorch_dump_path}'   )
 torch.save(model.state_dict()							,					snake_case_   )


if __name__ == "__main__":
      lowercase_  : Optional[int]								=					argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
      )
      parser.add_argument(
          '--config_file',
          default=None,
          type=str,
          required=True,
          help='The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.',
      )
      parser.add_argument(
          '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
      )
      lowercase_  : Optional[int]								=					parser.parse_args()
      convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)



from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase_    :			Any    =	logging.get_logger(__name__)

lowerCamelCase_    :			Dict    =	{
    """facebook/dpr-ctx_encoder-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-question_encoder-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-reader-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-ctx_encoder-multiset-base""": (
        """https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json"""
    ),
    """facebook/dpr-question_encoder-multiset-base""": (
        """https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json"""
    ),
    """facebook/dpr-reader-multiset-base""": (
        """https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json"""
    ),
}

class 							_UpperCamelCase					(       _A	):






			'''simple docstring'''

			__UpperCamelCase   :    Any          =			"""dpr"""

			def __init__(							self			:							str    ,						snake_case_			:							Any=3_0522    ,						snake_case_			:							List[str]=768    ,						snake_case_			:							List[str]=12    ,						snake_case_			:							Union[str, Any]=12    ,						snake_case_			:							Tuple=3072    ,						snake_case_			:							Any="gelu"    ,						snake_case_			:							Optional[Any]=0.1    ,						snake_case_			:							str=0.1    ,						snake_case_			:							Dict=512    ,						snake_case_			:							int=2    ,						snake_case_			:							Union[str, Any]=0.02    ,						snake_case_			:							str=1e-12    ,						snake_case_			:							List[str]=0    ,						snake_case_			:							Any="absolute"    ,						snake_case_			:							int = 0    ,						**snake_case_			:							int    ,						):
							super().__init__(pad_token_id=snake_case_    ,						**snake_case_   )

							UpperCamelCase_:				str						=    vocab_size
							UpperCamelCase_:				Optional[int]						=    hidden_size
							UpperCamelCase_:				int						=    num_hidden_layers
							UpperCamelCase_:				Any						=    num_attention_heads
							UpperCamelCase_:				int						=    hidden_act
							UpperCamelCase_:				Optional[Any]						=    intermediate_size
							UpperCamelCase_:				Tuple						=    hidden_dropout_prob
							UpperCamelCase_:				Tuple						=    attention_probs_dropout_prob
							UpperCamelCase_:				str						=    max_position_embeddings
							UpperCamelCase_:				Any						=    type_vocab_size
							UpperCamelCase_:				Tuple						=    initializer_range
							UpperCamelCase_:				List[Any]						=    layer_norm_eps
							UpperCamelCase_:				Tuple						=    projection_dim
							UpperCamelCase_:				Dict						=    position_embedding_type






